Ryan Cosner

Ryan Cosner

Caltech Mechanical Engineering PhD student studying controls, robotics, and safety.

Research

My research focuses on Controls, Robotics, and Safety.

Checkout these blog posts for brief and high-level introductions on some of these topics:

Publications

  • ICRA 2022: Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision

    Ryan K. Cosner*, Ivan D. Jimenez Rodriguez*, Tamas G. Molnar, Wyatt Ubellacker, Yisong Yue, Aaron D. Ames, Katherine L. Bouman.

    Abstract: With the increasing prevalence of complex vision sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement errors has become a difficult and essential part of modern robotics. This paper presents a self-supervised learning approach to safety-critical control. In particular, the uncertainty associated with stereo vision is estimated, and adapted online to new visual environments, wherein this estimate is leveraged in a safety-critical controller in a robust fashion. To this end, we propose an algorithm that exploits the structure of stereo-vision to learn an uncertainty estimate without the need for ground-truth data. We then robustify existing Control Barrier Function-based controllers to provide safety in the presence of this uncertainty estimate. We demonstrate the efficacy of our method on a quadrupedal robot in a variety of environments. When not using our method safety is violated. With offline training alone we observe the robot is safe, but overly-conservative. With our online method the quadruped remains safe and conservatism is reduced.

  • ICRA 2022: Enforcing Motion Primitive Transitions via Flow-Control Barrier Functions

    Wyatt Ubellacker, Ryan K. Cosner, Tamas G. Molnar, Andrew W. Singletary, Aaron D. Ames.

    Abstract: Transitions between individual dynamic primitive behaviors, termed “motion primitives,” play an essential role in realizing complex dynamic behaviors on robotic systems. This paper considers the flow, φt(x), under the action of a “primitive” control law as a means for determining which motion primitives can be transitioned between. To this end, it is taken into account that state uncertainty and modelling error present on real-world systems can result in unsafe deviations from the desired behavior. To combat unsafe behavior, a natural method is to enforce safety through the use of a control barrier function (CBF) to render a safe set forward invariant. This paper applies this concept to the flow of the system, and introduces a flow-control barrier function, (φ-CBF), as a minimally invasive filter to augment a nominal control law and provide input-to-state safety to a set over some finite time T. This method enforces desired transition behavior even in presence of uncertainties. The efficacy of the flow-control barrier function approach is experimentally demonstrated on a quadrupedal robot wherein case-study transitions are considered on a variety of terrains.

  • LCSS 2022: A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems

    Devansh R. Agrawal*, Hardik Parwana, Ryan K. Cosner*, Ugo Rosolia, Aaron D. Ames, Dmitra Panagou.

    Abstract: We present a multi-rate control architecture that leverages fundamental properties of differential flatness to synthesize controllers for safety-critical nonlinear dynamical systems subject to input constraints. We propose a two-layer architecture, where the high-level generates reference trajectories using a linear Model Predictive Controller, and the low-level tracks this reference using a feedback controller. The novelty lies in how we couple these layers, to achieve formal guarantees on recursive feasibility of the MPC problem, and safety of the nonlinear system. Furthermore, using differential-flatness, we provide a constructive means to synthesize the multi-rate controller, thereby removing the need to search for suitable Lyapunov or barrier functions, or to approximately linearize/discretize nonlinear dynamics. We show the synthesized controller is a convex optimization problem, making it amenable to real-time implementations. The method is demonstrated experimentally on a ground rover and a quadruped robotic system.

  • RAL 2022: Model-Free Safety-Critical Control for Robotic Systems

    Tamas G. Molnar, Ryan K. Cosner, Andrew W. Singletary, Wyatt Ubellacker, Aaron D. Ames. [pdf]

    Abstract: This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a – potentially complicated – high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an obstacle avoidance task with a Segway in high-fidelity simulation, as well as with a Drone and a Quadruped in hardware experiments.

  • IROS 2021: Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State

    Ryan K. Cosner, Andrew W. Singletary, Andrew J Taylor, Tamas G. Molnar, Katherine L. Bouman, Aaron D. Ames. [pdf]

    Abstract: The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements. In this paper we propose a rigorous framework for safety-critical control of systems with erroneous state estimates. We develop this framework by leveraging Control Barrier Functions (CBFs) and unifying the method of Backup Sets for synthesizing control invariant sets with robustness requirements—the end result is the synthesis of Measurement-Robust Control Barrier Functions (MR-CBFs). This provides theoretical guarantees on safe behavior in the presence of imperfect measurements and improved robustness over standard CBF approaches. We demonstrate the efficacy of this framework both in simulation and experimentally on a Segway platform using an onboard stereo-vision camera for state estimation.

  • LCSS 2021: Multi-rate control design under input constraints via fixed-time barrier functions

    Kunal Garg, Ryan K. Cosner, Ugo Rosolia, Aaron D. Ames, Dmitra Panagou. [pdf]

    Abstract: In this paper, we introduce the notion of periodic safety, which requires that the system trajectories periodically visit a subset of a forward-invariant safe set, and utilize it in a multi-rate framework where a high-level planner generates a reference trajectory that is tracked by a low-level controller under input constraints. We introduce the notion of fixed-time barrier functions which is leveraged by the proposed low-level controller in a quadratic programming framework. Then, we design a model predictive control policy for high-level planning with a bound on the rate of change for the reference trajectory to guarantee that periodic safety is achieved. We demonstrate the effectiveness of the proposed strategy on a simulation example, where the proposed fixed-time stabilizing low-level controller shows successful satisfaction of control objectives, whereas an exponentially stabilizing low-level controller fails.

  • L4DC 2020: Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety

    Noel Csomay-Shanklin*, Ryan K. Cosner*, Min Dai*, Andrew J. Taylor, Aaron D. Ames. [pdf]

    Abtract: This paper combines episodic learning and control barrier functions (CBFs) in the setting of bipedal locomotion. The safety guarantees that CBFs provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of Projection-to-State Safety paired with a machine learning framework in an attempt to learn the model uncertainty as it effects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem which requires precise foot placement while walking dynamically.

  • CoRL 2020: Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

    Sarah Dean, Andrew J. Taylor, Ryan K Cosner, Benjamin Recht, Aaron D. Ames. [pdf]

    Abstract: Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees. In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty. We define the notion of a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs when facing measurement model uncertainty. Furthermore, MR-CBFs are used to inform sampling methodologies for learning-based perception systems and quantify tolerable error in the resulting learned models. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system.